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Laplacian eigenmaps and principal curves for high resolution pseudotemporal ordering of single-cell RNA-seq profiles

https://doi.org/10.1101/027219
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Outgoing claims: the papers this work cites, builds on, or argues with.

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A tutorial on spectral clustering
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Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development
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Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis
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Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq
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Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation
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The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
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Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells
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Decoding the regulatory network of early blood development from single-cell gene expression measurements
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The Effect of Outliers in Independent Component Analysis
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Diffusion maps for high-dimensional single-cell analysis of differentiation data
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Sincell : an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq
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Fast and robust fixed-point algorithms for independent component analysis
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Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
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Gene ontology analysis for RNA-seq: accounting for selection bias
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Single Cell Genomics: Advances and Future Perspectives

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